mlsanityData to 14 Oct 2026

3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning

arXiv 2208.14796 ↗First posted 31 Aug 2022cs.CVcited by 0

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Abstract (arXiv)

Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, either abandon considerable local features during pooling operations or ignore many global features in the whole scene context. This paper explores new modules to simultaneously learn local-global features of scene point clouds that serve 3OD positively. To this end, we propose an effective 3OD network via simultaneous local-global feature learning (dubbed 3DLG-Detector). 3DLG-Detector has two key contributions. First, it develops a Dynamic Points Interaction (DPI) module that preserves effective local features during pooling. Besides, DPI is detachable and can be incorporated into existing 3OD networks to boost their performance. Second, it develops a Global Context Aggregation module to aggregate multi-scale features from different layers of the encoder to achieve scene context-awareness. Our method shows improvements over thirteen competitors in terms of detection accuracy and robustness on both the SUN RGB-D and ScanNet datasets. Source code will be available upon publication.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 43 of its 56 references that are in the map. First posted on arXiv 31 Aug 2022.

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43 of 56 references.

References · 43

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  12. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
31 more
  1. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 748
  2. 2021DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queriescited by 658
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  31. 2020SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloudcited by 16

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